这篇论文梳理了200多篇VIS4ML论文，告诉你人在机器学习中怎么用可视化注入知识。想了解人机协作的路径和原理，这篇综述是很好的起点。
对IEEE VIS会议过去十年200余篇VIS4ML论文进行系统调研。开发编码方案从ML特性、可视化、交互和动作四个视角分析。揭示了通过交互可视化将人类知识传递到ML工作流的不同路径。利用信息论成本效益分析解释VIS4ML现象，证实VA在ML工作流中的价值。
Understanding How Humans Inject Knowledge into Machine Learning Workflows through Visual Analytics
Visual analytics (VA) plays an increasingly important role in supporting machine learning (ML) workflows. In the field of visualization, such approaches and techniques are referred to as VIS4ML. While ML models are mostly learned automatically, the corresponding ML workflows receive a variety of human inputs, such as data labelling, feature engineering, model architecture designing, hyper-parameter tuning, and so on. In this work, we surveyed over 200 VIS4ML papers to gain an understanding of how humans inject their knowledge into ML workflows through interactive visualization. We collected a corpus of VIS4ML papers from the IEEE VIS conferences in the past decade. We developed a coding scheme to facilitate the literature research from four perspectives: characteristics of ML, visualization, interaction, and actions. The analysis of the coded dataset allows us to observe different pathways that transfer human knowledge to ML workflows via interactive visualization. Building on the analysis, we explain the phenomena of VIS4ML using the conceptual model that views VA as model building and the information-theoretic cost-benefit analysis that reasons VA as for optimizing ML workflows. This work provides unequivocal evidence showing the merits of using VA in ML workflows. The full list of surveyed papers, along with all analysis results and figures, is available at https://vis4ml4hd.github.io/ml-knowledge-inject-va/.